PIPP Phase I: Center for Ecosystems Data Integration and Pandemic Early Warning Systems

持续的COVID-19大流行凸显了设计、建立和实施下一代综合环境和临床监测系统的必要性，以监测和跟踪在地方、区域、国家和全球范围内具有大流行潜力的病原体的出现、传播和传播。除了病原体和疾病监测系统外，还迫切需要更强大的网络、基础设施和协议，以便在适当的时间向适当的人（包括地方、区域和国家卫生系统领导人和利益相关者）传达大流行病出现的风险。该项目的总体目标是解决与在西纽约开发和部署预警系统相关的挑战，该系统可以1）通过监测病毒和人类生态系统预测大流行的开始，2）向当地/区域社区传达疾病传播和大流行出现的风险，以指导预防措施和行为的设计和实施。为了推进这一目标，项目团队将利用PIPP第一阶段规划拨款的资金，为建立一个中心奠定基础，该中心将整合废水监测数据和临床鼻咽样本分析，以制定社区传染性病原体的基线，目的是持续跟踪其空间，时间和季节变化。项目小组还与纽约州伊利县卫生部合作，提议探索制定和实施更有效的战略，以传达大流行风险和包括行为改变在内的缓解建议。拟议的中心发展活动将包括有针对性的研究项目、讲习班和劳动力发展，包括培训和指导布法罗大学的两名博士后研究员和两名研究生。PIPP第一阶段项目将为在纽约西部建立生态系统数据集成和流行病预警系统中心奠定基础，其使命是：1）推进设计，开发和部署一个强大的、综合的流行病预警系统，2）促进社区参与，建立信任和伙伴关系，指导预防措施和行为的设计和实施，以减轻未来的流行病。为了推进这一目标，项目小组设想建立一个基于对地方/区域病毒和人类生态系统的监测和监视的疾病暴发和流行病预警系统。基于先进的病毒捕获和测序技术的可用性，项目团队建议开发更有效和更强大的检测方法和方案，用于常规监测废水和已知致病病毒的去识别人类鼻咽样本。 与此同时，项目小组建议评估收集的废水和鼻咽样本中不同病毒的相对丰度，以详细了解当地和区域病毒生态系统和病毒组，目的是确定这些生态系统中的扰动，这些扰动可以纳入监测系统和模型，作为病原体和疾病出现的早期指标。最后，项目团队建议开发数学和计算模型，包括基于机器学习的模型，以1）分析和解释病毒组数据，2）探索与流行病发作相关的临床人类数据与病毒组数据的整合，以揭示病毒感染模式以及当地社区和更广泛的西纽约地区疾病传播和流行病出现的早期迹象。该奖项得到了跨部门的大流行预防阶段预测情报（PIPP）计划的支持，该计划由生物科学（BIO），计算机信息科学与工程（CISE），工程（ENG）和社会部门共同资助。行为与经济科学（SBE）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The ongoing COVID-19 pandemic has highlighted the need to design, build, and implement the next-generation of integrated environmental and clinical surveillance systems for monitoring and tracking the emergence, spread, and transmission of pathogens with pandemic potential at the local, regional, national, and global scale. In addition to pathogen and disease surveillance systems, there is a critical need for more robust networks, infrastructure, and protocols to communicate the risks of pandemic emergence to the right people at the right time including local, regional, and national health system leaders and stakeholders. The overarching goal of this project is to address the challenges associated with the development and deployment of an early warning system in Western New York that could 1) predict the onset of a pandemic by monitoring viral and human ecosystems and 2) communicate the risks of disease transmission and pandemic emergence to local/regional communities to guide the design and implementation of preventative measures and behaviors. To advance this goal, the project team will leverage funding for this PIPP Phase I planning grant to lay the foundation for the establishment of a Center that will integrate data from wastewater surveillance and the analyses of clinical nasopharyngeal samples to develop a baseline of infectious pathogens in communities with the aim of continuously tracking their spatial, temporal, and seasonal variations. In collaboration with the New York State Erie County Department of Health, the project team also proposes to explore the development and implementation of more effective strategies to communicate pandemic risks and mitigation recommendations including behavioral changes. The proposed Center development activities will include targeted research projects, workshops, and workforce development including the training and mentoring of two post-doctoral fellows and two graduate students at the University at Buffalo.This PIPP Phase I project will lay the foundation for the establishment of the Center for Ecosystems Data Integration and Pandemic Early Warning Systems in Western New York with a mission to 1) advance the design, development, and deployment of a robust and integrated early warning system for pandemic preparedness and 2) catalyze community engagement to build trust and partnerships to guide the design and implementation of preventative measures and behaviors to mitigate future pandemics. To advance this goal, the project team envisions an early warning system for disease outbreaks and pandemics based on the monitoring and surveillance of local/regional viral and human ecosystems. Building upon the availability of cutting-edge viral capture and sequencing techniques, the project team proposes to develop more efficient and robust assays and protocols for the routine monitoring of wastewater and de-identified human nasopharyngeal samples for known pathogenic viruses. In parallel, the project team proposes to evaluate the relative abundance of different viruses in the collected wastewater and nasopharyngeal samples to generate a detailed understanding of the local and regional viral ecosystems and viromes with the goal of identifying perturbations in these ecosystems that could be integrated into monitoring systems and models to serve as early indicators of pathogen and disease emergence. Finally, the project team proposes to develop mathematical and computational models including machine learning based models to 1) analyze and interpret the virome data and 2) explore the integration of clinical human data associated with a pandemic onset with the virome data to uncover patterns of viral infections and early signs of disease transmission and pandemic emergence in the local communities and broader Western New York region. This award is supported by the cross-directorate Predictive Intelligence for Pandemic Prevention Phase I (PIPP) program, which is jointly funded by the Directorates for Biological Sciences (BIO), Computer Information Science and Engineering (CISE), Engineering (ENG), and Social, Behavioral and Economic Sciences (SBE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.